Hands-on_Ex03_2

4 Programming Animated Statistical Graphics with R

4.1 Terminology

• Frame: In an animated line graph, a frame shows the data at a specific time or category. As the animation moves to the next frame, the graph updates to show the new data.

• Animation Attributes: The animation attributes are the settings that control how the animation behaves — like how long each frame shows, how smoothly it moves between frames, and whether it restarts or continues from the current point.

4.2 Getting started

1. Loading the R packages

• plotly: R library for plotting interactive statistical graphs.

• gganimate: An ggplot extension for creating animated statistical graphs.

• gifski: To convert video frames to GIF animations.

• gapminder: An excerpt of the data available at Gapminder.org. We just want to use its country_colors scheme.

• tidyverse: A family of modern R packages specially designed to support data science, analysis and communication task.

• readxl: An R package used to read .xls and .xlsx into R.

pacman::p_load(plotly, gganimate, gifski, gapminder, tidyverse, readxl)

2. Importing the data

col <- c("Country", "Continent")
# to create a character vector containing the values "Country", "Continent"

globalPop <- read_xls("GlobalPopulation.xls", sheet="Data") %>%
# %>% is the pipe operator in R, it passes the result of one function into the next
  
  mutate(across(all_of(col), as.factor)) %>%
  # mutate(): to add or change columns in a data frame
  # across(col, as.factor): to convert the col to factor type
  # mutate_at() also can be used here
  
  mutate(Year = as.integer(Year))
  # convert year to integer

4.3 Animated Data Visualisation: gganimate

1. Building a static poplulation bubble plot

ggplot(globalPop, aes(x = Old, y = Young, size = Population, colour = Country)) +
  geom_point(alpha = 0.7, show.legend = FALSE) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 12)) +
  labs(title = 'Year: {frame_time}', x = '% Aged', y = '% Young') 

2. Building the animated bubble plot

• transition_time() is used to create transition through distinct states in time (i.e. Year)

• ease_aes() is used to control easing of aesthetics. The default is linear. Other methods are as the following:

  • quadratic

  • cubic

  • quartic

  • quintic

  • sine

  • circular

  • exponential

  • elastic

  • back

  • bounce

• view_*(): to control the panel behavior

• shadow_*(): to keep traces of previous or future frames

• enter_*()/exit_*() : to define how elements appear and disappear

ggplot(globalPop, aes(x = Old, y = Young, size = Population, colour = Country)) +
  geom_point(alpha = 0.7, show.legend = FALSE) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 12)) +
  labs(title = 'Year: {frame_time}', x = '% Aged', y = '% Young') +
  
  transition_time(Year) +       
  ease_aes('linear') + 
  view_follow(fixed_y = TRUE) + 
  # follow the data horizontally
  shadow_mark(past = TRUE, future = FALSE, alpha = 0.2) +
  # keep traces of previous frames
  enter_fade() + exit_shrink()

4.4 Animated Data Visualisation: plotly

1. Building an animated bubble plot: ggploy()

“show.legend = FALSE” doesn’t work here, so here needs to use “theme(legend.position=‘none’)”

gg <- ggplot(globalPop, aes(x = Old, y = Young, size = Population,
                            colour = Country)) +
  geom_point(aes(size = Population, frame = Year), alpha = 0.7,
             show.legend = FALSE) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 12)) +
  labs(x = '% Aged', y = '% Young') + 
  theme(legend.position='none')

ggplotly(gg)

2. Building an animated bubble plot: plot_ly()

bp <- globalPop %>%
  
  plot_ly(x = ~Old, y = ~Young, size = ~Population, color = ~Continent,
          sizes = c(2, 100), frame = ~Year, text = ~Country, hoverinfo = "text",
          type = 'scatter', mode = 'markers') %>%
  
  layout(showlegend = FALSE)

bp